import os
import numpy as np
import pandas as pd
from glob import glob
import shutil
# image
import cv2
from skimage.io import imread
# TensorFlow
import tensorflow as tf
from tensorflow.keras import layers, models
# Visualisation libraries
## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output
## progressbar
from tqdm import tqdm
## plotly
from plotly.offline import init_notebook_mode, iplot
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px
## seaborn
import seaborn as sns
## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
import matplotlib.colors as mcolors
from matplotlib.colors import LinearSegmentedColormap
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib import cm
# plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
This Data contains around 25k images of size 150x150 distributed under 6 categories. {'buildings' -> 0, 'forest' -> 1, 'glacier' -> 2, 'mountain' -> 3, 'sea' -> 4, 'street' -> 5 }
The Train, Test and Prediction data is separated in each zip files. There are around 14k images in Train, 3k in Test and 7k in Prediction.
def Header(Text, L = 100, C = 'Blue', T = 'White'):
BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
print(BACK[C] + FORE[T] + Style.NORMAL + Text + Style.RESET_ALL + ' ' + FORE[C] +
Style.NORMAL + (L- len(Text) - 1)*'=' + Style.RESET_ALL)
def Line(L=100, C = 'Blue'):
FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
print(FORE[C] + Style.NORMAL + L*'=' + Style.RESET_ALL)
Start_Path = 'natural_scenes'
Target = 'Class'
Data = pd.DataFrame({'Path': glob(os.path.join(Start_Path, '*', '*', '*', '*.jpg'))})
Data['File'] = Data['Path'].map(lambda x: x.split('\\')[-1])
Data['Class'] = Data['Path'].map(lambda x: x.split('\\')[-2].title())
Data['Dataset'] = Data['Path'].map(lambda x: x.split('\\')[-3].title().replace('Seg_',''))
#
Header('A Sample of the Dataframe')
Data = Data.reindex(sorted(Data.columns), axis=1)
display(Data.sample(10))
display(pd.DataFrame({'Number of Instances':[Data.shape[0]], 'Number of Attributes':[Data.shape[1]]}).style.hide_index())
def Path_Tree(startpath, Extension, sep = ' ' * 3):
Folders_with_Images = []
C = ['Red', 'Green', 'Magenta', 'Cyan']*len(os.listdir(startpath))
BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN,
'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
for root, _, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
if level >0:
indent = sep* (level)+ '└──'
print(indent + BACK[C[level]] + Fore.BLACK + Style.NORMAL + os.path.basename(root) + Style.RESET_ALL)
else:
title = os.path.basename(root)
print(Style.RESET_ALL + Fore.BLUE + Style.NORMAL + '=' * (len(title) +1) + Style.RESET_ALL)
print(Back.BLACK + Fore.CYAN + Style.NORMAL + title + Style.RESET_ALL)
print(Style.RESET_ALL + Fore.BLUE + Style.NORMAL + '=' * (len(title) +1)+ Style.RESET_ALL)
subindent = ' ' * 4 * (level + 1)
for file in files[:1]:
if file.endswith(Extension):
Folders_with_Images.append(root)
List = os.listdir(root)
print(level* sep, Fore.BLUE + Style.NORMAL +
'%i %s files: ' % (len(List), List[0].split('.')[-1].upper()) + Style.RESET_ALL +
'%s'%', '.join(List[:5]) + ', ...')
return Folders_with_Images
_ = Folders_with_Images = Path_Tree(Start_Path, '.jpg')
A Sample of the Dataframe ==========================================================================
| Class | Dataset | File | Path | |
|---|---|---|---|---|
| 16034 | Street | Train | 2111.jpg | natural_scenes\seg_train\seg_train\street\2111... |
| 3086 | Buildings | Train | 10744.jpg | natural_scenes\seg_train\seg_train\buildings\1... |
| 16419 | Street | Train | 5253.jpg | natural_scenes\seg_train\seg_train\street\5253... |
| 14292 | Sea | Train | 6962.jpg | natural_scenes\seg_train\seg_train\sea\6962.jpg |
| 6940 | Forest | Train | 5899.jpg | natural_scenes\seg_train\seg_train\forest\5899... |
| 3570 | Buildings | Train | 14568.jpg | natural_scenes\seg_train\seg_train\buildings\1... |
| 11421 | Mountain | Train | 3227.jpg | natural_scenes\seg_train\seg_train\mountain\32... |
| 4290 | Buildings | Train | 2285.jpg | natural_scenes\seg_train\seg_train\buildings\2... |
| 2817 | Street | Test | 22677.jpg | natural_scenes\seg_test\seg_test\street\22677.jpg |
| 10570 | Mountain | Train | 15269.jpg | natural_scenes\seg_train\seg_train\mountain\15... |
| Number of Instances | Number of Attributes |
|---|---|
| 17034 | 4 |
=============== natural_scenes =============== └──seg_pred └──seg_pred 7301 JPG files: 10004.jpg, 10005.jpg, 10012.jpg, 10013.jpg, 10017.jpg, ... └──seg_test └──seg_test └──buildings 437 JPG files: 20057.jpg, 20060.jpg, 20061.jpg, 20064.jpg, 20073.jpg, ... └──forest 474 JPG files: 20056.jpg, 20062.jpg, 20082.jpg, 20089.jpg, 20091.jpg, ... └──glacier 553 JPG files: 20059.jpg, 20087.jpg, 20092.jpg, 20109.jpg, 20111.jpg, ... └──mountain 525 JPG files: 20058.jpg, 20068.jpg, 20071.jpg, 20085.jpg, 20093.jpg, ... └──sea 510 JPG files: 20072.jpg, 20076.jpg, 20077.jpg, 20081.jpg, 20099.jpg, ... └──street 501 JPG files: 20066.jpg, 20067.jpg, 20069.jpg, 20070.jpg, 20075.jpg, ... └──seg_train └──seg_train └──buildings 2191 JPG files: 0.jpg, 10006.jpg, 1001.jpg, 10014.jpg, 10018.jpg, ... └──forest 2271 JPG files: 10007.jpg, 10010.jpg, 10020.jpg, 10030.jpg, 10037.jpg, ... └──glacier 2404 JPG files: 10.jpg, 100.jpg, 10003.jpg, 10009.jpg, 10011.jpg, ... └──mountain 2512 JPG files: 10000.jpg, 10001.jpg, 10002.jpg, 10008.jpg, 10023.jpg, ... └──sea 2274 JPG files: 1.jpg, 10016.jpg, 10041.jpg, 10053.jpg, 10061.jpg, ... └──street 2382 JPG files: 1000.jpg, 10015.jpg, 10019.jpg, 10022.jpg, 10036.jpg, ...
def DatasetDist(Inp, Target, PD):
Table = Inp[Target].value_counts().to_frame('Count').reset_index(drop = False).rename(columns = {'index':Target})
Table = Table.sort_values(by = [Target])
Table['Percentage'] = np.round(100*(Table['Count']/Table['Count'].sum()), 2)
fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, column_widths=PD['column_widths'],
specs=[[{"type": "table"},{"type": "pie"}]])
# Right
fig.add_trace(go.Pie(labels=Table[Target].values, values=Table['Count'].values,
pull=PD['pull'], textfont=dict(size= PD['textfont']),
marker=dict(colors = [Colors_dict[x] for x in Table[Target].unique()],
line=dict(color='black', width=1))), row=1, col=2)
fig.update_traces(hole=PD['hole'])
fig.update_layout(height = PD['height'], legend=dict(orientation=PD['legend_orientation']),
legend_title_text= PD['legend_title'])
# Left
T = Table.copy()
T['Percentage'] = T['Percentage'].map(lambda x: '%%%.2f' % x)
Temp = []
for i in T.columns:
Temp.append(T.loc[:,i].values)
fig.add_trace(go.Table(header=dict(values = list(Table.columns), line_color='darkslategray',
fill_color= PD['TableColors'][0], align=['center','center'],
font=dict(color='white', size=12), height=25), columnwidth = PD['tablecolumnwidth'],
cells=dict(values=Temp, line_color='darkslategray',
fill=dict(color= [PD['TableColors'][1], PD['TableColors'][1]]),
align=['center','center', 'center'], font_size=12, height=20)), 1, 1)
fig.update_layout(title={'text': '<b>' + PD['Title'] + '<b>', 'x':PD['title_x'],
'y':PD['title_y'], 'xanchor': 'center', 'yanchor': 'top'})
fig.show()
Colors_dict = dict(zip(Data[Target].unique().tolist(),
['DarkRed', 'DarkGreen', 'Silver', 'Sienna', 'SteelBlue','Gray']))
Pull = [0 for x in range((len(Data[Target].unique().tolist())-1))]
Pull.append(.05)
PD = dict(PieColors = Colors_dict, TableColors = ['DarkGreen','GhostWhite'], hole = .4,
column_widths=[0.5, 0.5],textfont = 14, height = 400, tablecolumnwidth = [.1, .05, .08], pull = Pull,
legend_title = Target, legend_orientation = 'v',
Title ='Train Set', title_x = 0.5, title_y = 0.84)
del Pull
DatasetDist(Data.loc[Data['Dataset'] == 'Train'], Target = Target, PD = PD)
PD.update(dict(TableColors = ['Indigo','GhostWhite'], Title ='Test Set'))
DatasetDist(Data.loc[Data['Dataset'] == 'Test'], Target = Target, PD = PD)
fig, ax = plt.subplots(4, 5 , figsize = (12, 12))
_ = fig.suptitle('A Sample of Train Dataset', fontweight='bold', fontsize = 18)
ax = ax.ravel()
for i, row in Data.loc[Data['Dataset'] == 'Train'].sample(len(ax)).reset_index(drop =True).iterrows():
_ = ax[i].imshow(imread(row['Path']))
_ = ax[i].set_title('%s' % row[Target], fontweight='bold', fontsize = 12, color = Colors_dict [row[Target]])
_ = ax[i].axis("off")
_ = ax[i].set_aspect(1)
fig.tight_layout()
batch_size = 128
(Img_Height, Img_Width, _) = imread(Data['Path'][0]).shape
Header('Train Images')
train_ds = tf.keras.preprocessing.image_dataset_from_directory(directory= 'natural_scenes\\seg_train\\seg_train',
shuffle=True,
seed=123,
image_size=(Img_Height, Img_Width),
batch_size=batch_size)
Header('Validation Images Data Generator', C = 'Green')
val_ds = tf.keras.preprocessing.image_dataset_from_directory(directory= 'natural_scenes\\seg_test\\seg_test',
image_size=(Img_Height, Img_Width),
batch_size=batch_size)
Line()
Train Images ======================================================================================= Found 14034 files belonging to 6 classes. Validation Images Data Generator =================================================================== Found 3000 files belonging to 6 classes. ====================================================================================================
A multi-layer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The algorithm at each iteration uses the SparseCategoricalCrossentropy to measure the loss, and then the gradient and the model update is calculated. At the end of this iterative process, we would reach a better level of agreement between test and predicted sets since the error would be lower from that of the first step.
num_classes = len(Data[Target].unique())
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip("horizontal",
input_shape=(Img_Height, Img_Width, 3)),
layers.experimental.preprocessing.RandomRotation(0.1),
layers.experimental.preprocessing.RandomZoom(0.1)])
model = models.Sequential(name = 'Multi_Class_MLP',
layers = [data_augmentation,
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(Img_Height, Img_Width, 3)),
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes)])
model.summary()
tf.keras.utils.plot_model(model, show_shapes=True, show_dtype=True,
show_layer_names=True, expand_nested = False, rankdir= 'LR')
Model: "Multi_Class_MLP" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= sequential (Sequential) (None, 150, 150, 3) 0 _________________________________________________________________ rescaling (Rescaling) (None, 150, 150, 3) 0 _________________________________________________________________ conv2d (Conv2D) (None, 150, 150, 16) 448 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 75, 75, 16) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 75, 75, 32) 4640 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 37, 37, 32) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 37, 37, 64) 18496 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 18, 18, 64) 0 _________________________________________________________________ flatten (Flatten) (None, 20736) 0 _________________________________________________________________ dense (Dense) (None, 128) 2654336 _________________________________________________________________ dense_1 (Dense) (None, 6) 774 ================================================================= Total params: 2,678,694 Trainable params: 2,678,694 Non-trainable params: 0 _________________________________________________________________
Compiling and fitting the model
# Number of iterations
IT = 21
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
# Training the model
history = model.fit(train_ds, validation_data=val_ds, epochs=IT, verbose = 0)
def Search_List(Key, List): return [s for s in List if Key in s]
Metrics_Names = {'loss':'Loss', 'accuracy':'Accuracy', 'mae':'MAE', 'mse':'MSE', 'recall': 'Recall'}
def Table_modify(df, Metrics_Names = Metrics_Names):
df = df.rename(columns = Metrics_Names)
df = df.reindex(sorted(df.columns), axis=1)
df.insert(loc = 0, column = 'Iteration', value = np.arange(0, df.shape[0]), allow_duplicates=False)
return df
Validation_Table = Search_List('val_',history.history.keys())
Train_Table = list(set( history.history.keys()) - set(Validation_Table))
Validation_Table = pd.DataFrame(np.array([history.history[x] for x in Validation_Table]).T, columns = Validation_Table)
Train_Table = pd.DataFrame(np.array([history.history[x] for x in Train_Table]).T, columns = Train_Table)
Validation_Table.columns = [x.replace('val_','') for x in Validation_Table.columns]
Train_Table = Table_modify(Train_Table)
Validation_Table = Table_modify(Validation_Table)
# Train Set Score
score = model.evaluate(train_ds, batch_size = batch_size, verbose = 0)
score = pd.DataFrame(score, index = model.metrics_names).T
score.index = ['Train Set Score']
# Validation Set Score
Temp = model.evaluate(val_ds, batch_size = batch_size, verbose = 0)
Temp = pd.DataFrame(Temp, index = model.metrics_names).T
Temp.index = ['Validation Set Score']
score = score.append(Temp)
score.rename(columns= Metrics_Names, inplace = True)
score = score.reindex(sorted(score.columns), axis=1)
display(score.style.set_precision(4))
| Accuracy | Loss | |
|---|---|---|
| Train Set Score | 0.8710 | 0.3594 |
| Validation Set Score | 0.8200 | 0.5582 |
def Plot_history(history, PD, Title = False, metrics_names = [x.title() for x in model.metrics_names]):
fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, column_widths=[0.6, 0.4],
specs=[[{"type": "scatter"},{"type": "table"}]])
# Left
Colors = ['OrangeRed', 'MidnightBlue', 'purple']
for j in range(len(metrics_names)):
fig.add_trace(go.Scatter(x= history['Iteration'].values, y= history[metrics_names[j]].values,
line=dict(color=Colors[j], width= 1.5), name = metrics_names[j]), 1, 1)
fig.update_layout(legend=dict(x=0, y=1.1, traceorder='reversed', font_size=12),
dragmode='select', plot_bgcolor= 'white', height=600, hovermode='closest',
legend_orientation='h')
fig.update_xaxes(range=[history.Iteration.min(), history.Iteration.max()],
showgrid=True, gridwidth=1, gridcolor='Lightgray',
showline=True, linewidth=1, linecolor='Lightgray', mirror=True, row=1, col=1)
fig.update_yaxes(range=[0, PD['yLim']], showgrid=True, gridwidth=1, gridcolor='Lightgray',
showline=True, linewidth=1, linecolor='Lightgray', mirror=True, row=1, col=1)
# Right
if not PD['Table_Rows'] == None:
ind = np.linspace(0, history.shape[0], PD['Table_Rows'], endpoint = False).round(0).astype(int)
ind = np.append(ind, history.index[-1])
history = history[history.index.isin(ind)]
T = history.copy()
T[metrics_names] = T[metrics_names].applymap(lambda x: '%.4e' % x)
Temp = []
for i in T.columns:
Temp.append(T.loc[:,i].values)
TableColors = PD['TableColors']
fig.add_trace(go.Table(header=dict(values = list(history.columns), line_color=TableColors[0],
fill_color=TableColors[0], align=['center','center'], font=dict(color=TableColors[1], size=12), height=25),
columnwidth = PD['tablecolumnwidth'], cells=dict(values=Temp, line_color=TableColors[0],
fill=dict(color=[TableColors[1], TableColors[1]]),
align=['center', 'center'], font_size=12,height=20)), 1, 2)
if Title != False:
fig.update_layout(plot_bgcolor= 'white',
title={'text': Title, 'x':0.46, 'y':0.94, 'xanchor': 'center', 'yanchor': 'top'},
yaxis_title='Frequency')
fig.show()
PD = dict(Table_Rows = 25, yLim = 2, tablecolumnwidth = [0.3, 0.4, 0.4], TableColors = ['DarkSlateGray','White'])
Plot_history(Train_Table, Title = 'Train Set', PD = PD)
Plot_history(Validation_Table, Title = 'Validation Set', PD = PD)
val_images = []
val_labels = []
for x, y in list(val_ds):
val_images.extend(x)
val_labels.extend(y)
Pred = model.predict(val_ds)
class_names = val_ds.class_names
class_names = [x.title() for x in class_names]
Prob = np.array(tf.nn.softmax(Pred[0]))
for i in tqdm(range(len(Pred[1:]))):
Temp = np.array( tf.nn.softmax(Pred[i]))
Prob = np.column_stack((Prob, Temp))
Pred = pd.DataFrame(data = Prob.T, columns = class_names)
Pred['Actual Label'] = [class_names[x] for x in val_labels]
100%|███████████████████████████████████████████████████████████████████████████| 2999/2999 [00:00<00:00, 27011.85it/s]
display(Pred.round(2))
| Buildings | Forest | Glacier | Mountain | Sea | Street | Actual Label | |
|---|---|---|---|---|---|---|---|
| 0 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.97 | Glacier |
| 1 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.97 | Street |
| 2 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | Sea |
| 3 | 0.02 | 0.00 | 0.05 | 0.87 | 0.06 | 0.00 | Street |
| 4 | 0.02 | 0.16 | 0.00 | 0.00 | 0.00 | 0.82 | Mountain |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 2995 | 0.37 | 0.02 | 0.00 | 0.60 | 0.00 | 0.01 | Sea |
| 2996 | 0.28 | 0.00 | 0.04 | 0.01 | 0.66 | 0.01 | Sea |
| 2997 | 0.99 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | Sea |
| 2998 | 0.31 | 0.00 | 0.11 | 0.45 | 0.05 | 0.08 | Sea |
| 2999 | 0.00 | 0.00 | 0.81 | 0.14 | 0.04 | 0.00 | Sea |
3000 rows × 7 columns